We applied Focal Loss [1]} and Weighted Loss [2]}, [3]} to the inner-loop of optimization-based methods at inference time only. Results in Figure REF  and Appendix REF  show that overall, Focal Loss is not as effective as ROS+ techniques. However, ROS+ and Weighted Loss perform similarly, suggesting a similar effect on the imbalanced task. The advantage of using ROS/ROS+ is versatility; any FSL algorithm can use ROS, while algorithm-level balancing approaches, such as Weighted Loss, do not straightforwardly extend to metric-learning methods.
